Article

VideoReach: an online video recommendation system

Authors:

Tao Mei,

Bo Yang,

Xian-Sheng Hua,

Linjun Yang,

Shi-Qiang Yang,

Shipeng LiAuthors Info & Claims

SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 767 - 768

https://doi.org/10.1145/1277741.1277899

Published: 23 July 2007 Publication History

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Abstract

This paper presents a novel online video recommendation system called VideoReach, which alleviates users' efforts on finding the most relevant videos according to current viewings without a sufficient collection of user profiles as required in traditional recommenders. In this system, video recommendation is formulated as finding a list of relevant videos in terms of multimodal relevance (i.e. textual, visual, and aural relevance) and user click-through. Since different videos have different intra-weights of relevance within an individual modality and inter-weights among different modalities, we adopt relevance feedback to automatically find optimal weights by user click-though, as well as an attention fusion function to fuse multimodal relevance. We use 20 clips as the representative test videos, which are searched by top 10 queries from more than 13k online videos, and report superior performance compared with an existing video site.

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MovieFinder. http://www.moviefinder.com/.

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Jin QLiu YYuan YHan BQian FYarosh S(2024)Virtual Reality, Real Pedagogy: A Contextual Inquiry of Instructor Practices with VR VideoProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642510(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642510
Zeng KLiu WLiu D(2024)Solving the Short Video Assignment Problem via Federated Learning and Group Multi-Role Assignment2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)10.1109/ISPA63168.2024.00124(934-939)Online publication date: 30-Oct-2024
https://doi.org/10.1109/ISPA63168.2024.00124
Lubos SFelfernig ATautschnig M(2023)An overview of video recommender systems: state-of-the-art and research issuesFrontiers in Big Data10.3389/fdata.2023.12816146Online publication date: 30-Oct-2023
https://doi.org/10.3389/fdata.2023.1281614
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Index Terms

VideoReach: an online video recommendation system
1. Information systems
  1. World Wide Web
    1. Web applications
    2. Web services

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Information & Contributors

Information

Published In

SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval

July 2007

946 pages

ISBN:9781595935977

DOI:10.1145/1277741

General Chairs:
Wessel Kraaij
TNO, The Netherlands
,
Arjen P. de Vries
CWI, The Netherlands
,
Program Chairs:
Charles L. A. Clarke
University of Waterloo, Canada
,
Norbert Fuhr
University of Duisburg-Essen, Germany
,
Noriko Kando
National Institute of Informatics, Japan

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 23 July 2007

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SIGIR07: The 30th Annual International SIGIR Conference

July 23 - 27, 2007

Amsterdam, The Netherlands

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Jin QLiu YYuan YHan BQian FYarosh S(2024)Virtual Reality, Real Pedagogy: A Contextual Inquiry of Instructor Practices with VR VideoProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642510(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642510
Zeng KLiu WLiu D(2024)Solving the Short Video Assignment Problem via Federated Learning and Group Multi-Role Assignment2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)10.1109/ISPA63168.2024.00124(934-939)Online publication date: 30-Oct-2024
https://doi.org/10.1109/ISPA63168.2024.00124
Lubos SFelfernig ATautschnig M(2023)An overview of video recommender systems: state-of-the-art and research issuesFrontiers in Big Data10.3389/fdata.2023.12816146Online publication date: 30-Oct-2023
https://doi.org/10.3389/fdata.2023.1281614
Adesh ADeepak GSanthanavijayan A(2023)SemVidRec: A Semantic Approach to Annotations Driven Video Recommendation Model Incorporating Machine IntelligenceComputational Intelligence and Network Systems10.1007/978-3-031-48984-6_1(1-13)Online publication date: 16-Dec-2023
https://doi.org/10.1007/978-3-031-48984-6_1
Kang SChung K(2022)Preference-Tree-Based Real-Time Recommendation SystemEntropy10.3390/e2404050324:4(503)Online publication date: 2-Apr-2022
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Bhuyan HBrahma BRao P(2022)Multi-level feature learning approaches for video recommendation2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)10.1109/ASSIC55218.2022.10088325(1-6)Online publication date: 19-Nov-2022
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Deldjoo YSchedl MCremonesi PPasi G(2020)Recommender Systems Leveraging Multimedia ContentACM Computing Surveys10.1145/340719053:5(1-38)Online publication date: 28-Sep-2020
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Dong JWang XZhang LXu CYang GLi X(2019)Feature Re-Learning with Data Augmentation for Video Relevance PredictionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2947442(1-1)Online publication date: 2019
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Zhang WWang YChen HWei X(2019)An Efficient Personalized Video Recommendation Algorithm Based on Mixed Mode2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)10.1109/IUCC/DSCI/SmartCNS.2019.00088(367-373)Online publication date: Oct-2019
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Liu YLyu CLiu ZTao D(2019)Building Effective Short Video Recommendation2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)10.1109/ICMEW.2019.00126(651-656)Online publication date: Jul-2019
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Cited By

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Recommendations

Real-time Video Recommendation Exploration

Contextual Video Recommendation by Multimodal Relevance and User Feedback

Video recommendation based on multi-modal information and multiple kernel

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